Learning Convolutional Features and Text Information to Draw Image
Article No.: 10, Pages 1 - 5
Abstract
In this paper, a more effective and general joint exploration method (JEM) is proposed to synthesize images. By combining the technology of image segmentation, feature extraction, and image synthesis, high-quality images can be generated based on the text description and the convolutional segmentation information. Experiments on the Oxford-102 dataset show that our method is more effective than the GAN-CLS-INT method proposed recently. It also shows that in the training process, using VGG for feature extraction has a faster convergence speed than using AlexNet. Simultaneously, we demonstrate that the segmentation image's background information plays an active role in the training process.
References
[1]
I. J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. C. Courville, and Y. Bengio, “Generative adversarial nets,” Montreal, Quebec, Canada, In: Neural Information Processing Systems 27, pp. 2672–2680, December 2014.
[2]
A. Radford, L. Metz, and S. Chintala, “Unsupervised representation learning with deep convolutional generative adversarial networks,” San Juan, Puerto Rico, In: International Conference on Learning Representations, May, 2016.
[3]
M. Mirza, and S. Osindero, “Conditional generative adversarial nets,” In: arXiv:1411.1784, 2014.
[4]
S. E. Reed, Z. Akata, X. Yan, L. Logeswaran, B. Schiele, and H. Lee, “Generative adversarial text to image synthesis,” Venice, Italy, In: International Conference on Computer Vision, pp. 2242–2251, October, 2017.
[5]
K. He, G. Gkioxari, P. Dollár, and R. B. Girshick, “mask R-CNN,” Venice, Italy, In: International Conference on Computer Vision, pp. 2980–2988, October, 2017.
[6]
A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks,” Lake Tahoe, Nevada, United States, In: Advances in Neural Information Processing Systems, pp. 1106–1114, December, 2012.
[7]
K.Simonyan, and A. Zisserman., “Very Deep Convolutional Networks for Large-Scale Image Recognition,” San Diego, CA, USA, In: International Conference on Learning Representations, May, 2015.
[8]
R.B. Girshick, J. Donahue, T. Darrell, and J. Malik., “Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation,” Columbus, OH, USA, In: Computer Vision and Pattern Recognition, pp. 580–587, June, 2014.
[9]
R.B. Girshich., “Fast R-CNN,” Santiago, Chile, In: International Conference on Computer Vision, pp. 1440–1448, December, 2015.
[10]
S. Ren, K. He, R.B. Girshick, and J. Sun., “Faster R-CNN: Towards Real-Time Objects Detection with Region Proposal Networks,” Montreal, Quebec, Canada, In: Advances in Neural Information Processing Systems, pp. 91–99, December, 2015.
[11]
V. Nair, and G. E. Hinton, “Rectified linear units improve restricted boltzmann machines,” Haifa, Israel, In: International Conference on Machine Learning, pp. 807–814, June, 2010.
[12]
N. Srivastava, G. E. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: a simple way to prevent neural networks from overfitting,” In: J. Mach. Learn. Res., vol 15, no 1, pp. 1929–1958, 2014.
[13]
R. Kiros, Y. Zhu, R. Salakhutdinov, R. S. Zemel, A. Torralba, R. Urtasun, and S. Fidler, “Skip-Thought Vectors,” Montreal, Quebec, Canada, In: Advances in Neural Information Processing Systems, pp. 3294–3302, December, 2015.
[14]
M. D. Zeiler, and R. Fergus, “Visualizing and understanding convolutional networks,” Zurich, Switzerland, In: European Conference on Computer Vision, pp. 828–833, September, 2014.
[15]
S. Ioffe, and C. Szegedy, “Batch normalization: Accelerating deep network training by reducing internal covariate shift,” Lille, France, In: International Conference on Machine Learning, 448–456, July, 2015.
[16]
D. P. Kingma, and J. Ba, “Adam: a method for stochastic optimization,” San Diego, CA, USA, In: International Conference on Learning Representations, May, 2015.
[17]
M.-E. Nilsback, and A. Zisserman., “Automated flower classification over a large number of classes,” Bhubaneswar, India, In: Indian Conference on Computer Vision, Graphics and Image Processing, pp. 722–729, December, 2008.
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Published In
January 2021
1142 pages
ISBN:9781450389570
DOI:10.1145/3448734
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Association for Computing Machinery
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Published: 17 May 2021
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CONF-CDS 2021
CONF-CDS 2021: The 2nd International Conference on Computing and Data Science
January 28 - 30, 2021
CA, Stanford, USA
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